Intelligent Threat Correlation and Risk Assessment for Enterprise Cloud Security Using Generative AI and Behavioral Analytics Framework

Authors

  • Dr. Hadi Santoso Faculty of Computer Science, Universitas Mercu Buana, Jakarta, Indonesia Author

DOI:

https://doi.org/10.15662/IJARCST.2024.0706044

Keywords:

Enterprise Cloud Security, Generative Artificial Intelligence, Behavioral Analytics, Threat Correlation, Risk Assessment, Cybersecurity, Anomaly Detection, Security Information and Event Management, Cloud Threat Intelligence, Incident Response

Abstract

The rapid adoption of cloud computing has significantly transformed enterprise information technology infrastructure while introducing complex cybersecurity challenges, including sophisticated cyberattacks, insider threats, unauthorized access, data breaches, and advanced persistent threats. Traditional security monitoring systems often struggle to identify interconnected threats because they rely on isolated alerts, predefined rules, and signature-based detection mechanisms. This research proposes an intelligent threat correlation and risk assessment framework integrating Generative Artificial Intelligence (GenAI) and behavioral analytics to improve enterprise cloud security. The proposed framework combines cloud security logs, user activity records, network traffic patterns, identity access information, and threat intelligence to identify abnormal behaviors and correlate multiple security events. Generative AI supports contextual threat interpretation, automated incident summarization, and security recommendations, while behavioral analytics establishes normal activity patterns and detects deviations. A risk assessment mechanism evaluates detected threats according to severity, probability, potential business impact, and asset criticality. The framework aims to improve threat detection accuracy, reduce false-positive alerts, strengthen incident prioritization, and support proactive security decision-making. The research also discusses implementation considerations, privacy protection, model reliability, scalability, and human supervision. The proposed approach provides an intelligent and adaptive foundation for strengthening enterprise cloud security against evolving cyber threats

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Published

2024-12-20

How to Cite

Intelligent Threat Correlation and Risk Assessment for Enterprise Cloud Security Using Generative AI and Behavioral Analytics Framework. (2024). International Journal of Advanced Research in Computer Science & Technology(IJARCST), 7(6), 11542-11550. https://doi.org/10.15662/IJARCST.2024.0706044